A post-processing pipeline to reconstruct the developing fetal brain using low-resolution MRI

Introduction. Development of the fetal brain surface with concomitant gyrification is one of the major maturational processes of the human brain. First delineated by postmortem studies or by ultrasound, MRI has recently become a powerful tool for studying in vivo the structural correlates of brain maturation. However, the quantitative measurement of fetal brain development is a major challenge because of the movement of the fetus inside the amniotic cavity, the poor spatial resolution, the partial volume effect and the changing appearance of the developing brain. Today extensive efforts are made to deal with the “post-acquisition” reconstruction of high-resolution 3D fetal volumes based on several acquisitions with lower resolution (Rousseau, F., 2006; Jiang, S., 2007). We here propose a framework devoted to the segmentation of the basal ganglia, the gray-white tissue segmentation, and in turn the 3D cortical reconstruction of the fetal brain. Method. Prenatal MR imaging was performed with a 1-T system (GE Medical Systems, Milwaukee) using single shot fast spin echo (ssFSE) sequences in fetuses aged from 29 to 32 gestational weeks (slice thickness 5.4mm, in plane spatial resolution 1.09mm). For each fetus, 6 axial volumes shifted by 1 mm were acquired (about 1 min per volume). First, each volume is manually segmented to extract fetal brain from surrounding fetal and maternal tissues. Inhomogeneity intensity correction and linear intensity normalization are then performed. A high spatial resolution image of isotropic voxel size of 1.09 mm is created for each fetus as previously published by others (Rousseau, F., 2006). B-splines are used for the scattered data interpolation (Lee, 1997). Then, basal ganglia segmentation is performed on this super reconstructed volume using active contour framework with a Level Set implementation (Bach Cuadra, M., 2010). Once basal ganglia are removed from the image, brain tissue segmentation is performed (Bach Cuadra, M., 2009). The resulting white matter image is then binarized and further given as an input in the Freesurfer software (http://surfer.nmr.mgh.harvard.edu/) to provide accurate three-dimensional reconstructions of the fetal brain. Results. High-resolution images of the cerebral fetal brain, as obtained from the low-resolution acquired MRI, are presented for 4 subjects of age ranging from 29 to 32 GA. An example is depicted in Figure 1. Accuracy in the automated basal ganglia segmentation is compared with manual segmentation using measurement of Dice similarity (DSI), with values above 0.7 considering to be a very good agreement. In our sample we observed DSI values between 0.785 and 0.856. We further show the results of gray-white matter segmentation overlaid on the high-resolution gray-scale images. The results are visually checked for accuracy using the same principles as commonly accepted in adult neuroimaging. Preliminary 3D cortical reconstructions of the fetal brain are shown in Figure 2. Conclusion. We hereby present a complete pipeline for the automated extraction of accurate three-dimensional cortical surface of the fetal brain. These results are preliminary but promising, with the ultimate goal to provide “movie” of the normal gyral development. In turn, a precise knowledge of the normal fetal brain development will allow the quantification of subtle and early but clinically relevant deviations. Moreover, a precise understanding of the gyral development process may help to build hypotheses to understand the pathogenesis of several neurodevelopmental conditions in which gyrification have been shown to be altered (e.g. schizophrenia, autism…). References. Rousseau, F. (2006), 'Registration-Based Approach for Reconstruction of High-Resolution In Utero Fetal MR Brain images', IEEE Transactions on Medical Imaging, vol. 13, no. 9, pp. 1072-1081. Jiang, S. (2007), 'MRI of Moving Subjects Using Multislice Snapshot Images With Volume Reconstruction (SVR): Application to Fetal, Neonatal, and Adult Brain Studies', IEEE Transactions on Medical Imaging, vol. 26, no. 7, pp. 967-980. Lee, S. (1997), 'Scattered data interpolation with multilevel B-splines', IEEE Transactions on Visualization and Computer Graphics, vol. 3, no. 3, pp. 228-244. Bach Cuadra, M. (2010), 'Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain', ISMRM Conference. Bach Cuadra, M. (2009), 'Brain tissue segmentation of fetal MR images', MICCAI.

Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Anouk André, Meritxell Bach Cuadra, Gabriele Vincenzo Bonanno, Marie Schaer, Jean-Philippe Thiran

Motivation. The study of human brain development in its early stage is today possible thanks to in vivo fetal magnetic resonance imaging (MRI) techniques. A quantitative analysis of fetal cortical surface represents a new approach which can be used as a marker of the cerebral maturation (as gyration) and also for studying central nervous system pathologies [1]. However, this quantitative approach is a major challenge for several reasons. First, movement of the fetus inside the amniotic cavity requires very fast MRI sequences to minimize motion artifacts, resulting in a poor spatial resolution and/or lower SNR. Second, due to the ongoing myelination and cortical maturation, the appearance of the developing brain differs very much from the homogenous tissue types found in adults. Third, due to low resolution, fetal MR images considerably suffer of partial volume (PV) effect, sometimes in large areas. Today extensive efforts are made to deal with the reconstruction of high resolution 3D fetal volumes [2,3,4] to cope with intra-volume motion and low SNR. However, few studies exist related to the automated segmentation of MR fetal imaging. [5] and [6] work on the segmentation of specific areas of the fetal brain such as posterior fossa, brainstem or germinal matrix. First attempt for automated brain tissue segmentation has been presented in [7] and in our previous work [8]. Both methods apply the Expectation-Maximization Markov Random Field (EM-MRF) framework but contrary to [7] we do not need from any anatomical atlas prior. Data set & Methods. Prenatal MR imaging was performed with a 1-T system (GE Medical Systems, Milwaukee) using single shot fast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms, FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatial resolution 1.09mm). Each fetus has 6 axial volumes (around 15 slices per volume), each of them acquired in about 1 min. Each volume is shifted by 1 mm with respect to the previous one. Gestational age (GA) ranges from 29 to 32 weeks. Mother is under sedation. Each volume is manually segmented to extract fetal brain from surrounding maternal tissues. Then, in-homogeneity intensity correction is performed using [9] and linear intensity normalization is performed to have intensity values that range from 0 to 255. Note that due to intra-tissue variability of developing brain some intensity variability still remains. For each fetus, a high spatial resolution image of isotropic voxel size of 1.09 mm is created applying [2] and using B-splines for the scattered data interpolation [10] (see Fig. 1). Then, basal ganglia (BS) segmentation is performed on this super reconstructed volume. Active contour framework with a Level Set (LS) implementation is used. Our LS follows a slightly different formulation from well-known Chan-Vese [11] formulation. In our case, the LS evolves forcing the mean of the inside of the curve to be the mean intensity of basal ganglia. Moreover, we add local spatial prior through a probabilistic map created by fitting an ellipsoid onto the basal ganglia region. Some user interaction is needed to set the mean intensity of BG (green dots in Fig. 2) and the initial fitting points for the probabilistic prior map (blue points in Fig. 2). Once basal ganglia are removed from the image, brain tissue segmentation is performed as described in [8]. Results. The case study presented here has 29 weeks of GA. The high resolution reconstructed volume is presented in Fig. 1. The steps of BG segmentation are shown in Fig. 2. Overlap in comparison with manual segmentation is quantified by the Dice similarity index (DSI) equal to 0.829 (values above 0.7 are considered a very good agreement). Such BG segmentation has been applied on 3 other subjects ranging for 29 to 32 GA and the DSI has been of 0.856, 0.794 and 0.785. Our segmentation of the inner (red and blue contours) and outer cortical surface (green contour) is presented in Fig. 3. Finally, to refine the results we include our WM segmentation in the Freesurfer software [12] and some manual corrections to obtain Fig.4. Discussion. Precise cortical surface extraction of fetal brain is needed for quantitative studies of early human brain development. Our work combines the well known statistical classification framework with the active contour segmentation for central gray mater extraction. A main advantage of the presented procedure for fetal brain surface extraction is that we do not include any spatial prior coming from anatomical atlases. The results presented here are preliminary but promising. Our efforts are now in testing such approach on a wider range of gestational ages that we will include in the final version of this work and studying as well its generalization to different scanners and different type of MRI sequences. References. [1] Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau, Acad. Rad. 13(9), 2006, [3] Jiang, IEEE TMI 2007. [4] Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio. Eng. 51(4) (2004). [6] Habas, MICCAI (Pt. 1) 2008. [7] Bertelsen, ISMRM 2009 [8] Bach Cuadra, IADB, MICCAI 2009. [9] Styner, IEEE TMI 19(39 (2000). [10] Lee, IEEE Trans. Visual. And Comp. Graph. 3(3), 1997, [11] Chan, IEEE Trans. Img. Proc, 10(2), 2001 [12] Freesurfer, http://surfer.nmr.mgh.harvard.edu.

2010

Quantitative morphometry analysis of the fetal brain using clinical MR imaging

Meritxell Bach Cuadra, Gabriele Vincenzo Bonanno, Marie Schaer, Jean-Philippe Thiran

In vivo fetal magnetic resonance imaging provides a unique approach for the study of early human brain development [1]. In utero cerebral morphometry could potentially be used as a marker of the cerebral maturation and help to distinguish between normal and abnormal development in ambiguous situations. However, this quantitative approach is a major challenge because of the movement of the fetus inside the amniotic cavity, the poor spatial resolution provided by very fast MRI sequences and the partial volume effect. Extensive efforts are made to deal with the reconstruction of high-resolution 3D fetal volumes based on several acquisitions with lower resolution [2,3,4]. Frameworks were developed for the segmentation of specific regions of the fetal brain such as posterior fossa, brainstem or germinal matrix [5,6], or for the entire brain tissue [7,8], applying the Expectation-Maximization Markov Random Field (EM-MRF) framework. However, many of these previous works focused on the young fetus (i.e. before 24 weeks) and use anatomical atlas priors to segment the different tissue or regions. As most of the gyral development takes place after the 24th week, a comprehensive and clinically meaningful study of the fetal brain should not dismiss the third trimester of gestation. To cope with the rapidly changing appearance of the developing brain, some authors proposed a dynamic atlas [8]. To our opinion, this approach however faces a risk of circularity: each brain will be analyzed / deformed using the template of its biological age, potentially biasing the effective developmental delay. Here, we expand our previous work [9] to propose post-processing pipeline without prior that allow a comprehensive set of morphometric measurement devoted to clinical application. Data set & Methods: Prenatal MR imaging was performed with a 1-T system (GE Medical Systems, Milwaukee) using single shot fast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms, FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatial resolution 1.09mm). For each fetus, 6 axial volumes shifted by 1 mm were acquired under mother’s sedation (about 1min per volume). First, each volume is segmented semi-automatically using region-growing algorithms to extract fetal brain from surrounding maternal tissues. Inhomogeneity intensity correction [10] and linear intensity normalization are then performed. Brain tissues (CSF, GM and WM) are then segmented based on the low-resolution volumes as presented in [9]. A high-resolution image with isotropic voxel size of 1.09 mm is created as proposed in [2] and using B-splines for the scattered data interpolation [11]. Basal ganglia segmentation is performed using a levet set implementation on the high-resolution volume [12]. The resulting white matter image is then binarized and given as an input in FreeSurfer software (http://surfer.nmr.mgh.harvard.edu) to provide topologically accurate three-dimensional reconstructions of the fetal brain according to the local intensity gradient. References: [1] Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau, Acad. Rad. 13(9), 2006. [3] Jiang, IEEE TMI 2007. [4] Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio. Eng. 51(4) 2004. [6] Habas, MICCAI 2008. [7] Bertelsen, ISMRM 2009. [8] Habas, Neuroimage 53(2) 2010. [9] Bach Cuadra, IADB, MICCAI 2009. [10] Styner, IEEE TMI 19(39 (2000). [11] Lee, IEEE Trans. Visual. And Comp. Graph. 3(3), 1997. [12] Bach Cuadra, ISMRM 2010.

2011

Dense Deformation Field Estimation for Atlas Registration using the Active Contour Framework

Meritxell Bach Cuadra, Xavier Bresson, Valérie Duay, Jean-Philippe Thiran

In this paper, we propose a new paradigm to carry out the registration task with a dense deformation field derived from the optical flow model and the active contour method. The proposed framework merges different tasks such as segmentation, regularization, incorporation of prior knowledge and registration into a single framework. The active contour model is at the core of our framework even if it is used in a different way than the standard approaches. Indeed, active contours are a well-known technique for image segmentation. This technique consists in finding the curve which minimizes an energy functional designed to be minimal when the curve has reached the object contours. That way, we get accurate and smooth segmentation results. So far, the active contour model has been used to segment objects lying in images from boundary-based, region-based or shape-based information. Our registration technique will profit of all these families of active contours to determine a dense deformation field defined on the whole image. A well-suited application of our model is the atlas registration in medical imaging which consists in automatically delineating anatomical structures. We present results on 2D synthetic images to show the performances of our non rigid deformation field based on a natural registration term. We also present registration results on real 3D medical data with a large space occupying tumor substantially deforming surrounding structures, which constitutes a high challenging problem.